/rasa-nlu-integration

name: rasa-nlu-integration
description: Rasa NLU pipeline configuration and training for intent and entity extraction
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash
  - Glob
  - Grep
graph:
  domains: [domain:software-engineering]
  specializations: [specialization:ai-agents-conversational]
  skillAreas: [skill-area:natural-language-processing, skill-area:ml-fine-tuning]
  roles: [role:ml-engineer, role:backend-engineer]
  workflows: [workflow:ml-model-lifecycle, workflow:feature-development]

Rasa NLU Integration Skill

Capabilities

• Configure Rasa NLU pipelines
• Design training data in Rasa format
• Set up intent classification components
• Configure entity extraction (DIETClassifier)
• Implement pipeline optimization
• Set up model evaluation and testing

Target Processes

• intent-classification-system
• chatbot-design-implementation

Implementation Details

Pipeline Components

1. **Tokenizers**: WhitespaceTokenizer, SpacyTokenizer 2. **Featurizers**: CountVectorsFeaturizer, SpacyFeaturizer 3. **Classifiers**: DIETClassifier, FallbackClassifier 4. **Entity Extractors**: DIETClassifier, SpacyEntityExtractor

Configuration Files

• config.yml: Pipeline configuration
• nlu.yml: Training data
• domain.yml: Intents and entities

Configuration Options

• Pipeline component selection
• Featurizer settings
• Classifier parameters
• Entity extraction rules
• Fallback thresholds

Best Practices

• Start with recommended pipelines
• Tune based on domain
• Balance complexity vs performance
• Regular model retraining

Dependencies

• rasa